Towards Universal Representation-Based Process Control

The paper proposes a new approach to window‑level monitoring of temporal processes by treating it as a reference‑based hypothesis test. Instead of relying on predefined parametric models, the method uses an empirical reference distribution derived from task‑ or domain‑specific data, combined with pretrained time‑series encoders, kernel density estimation, and conformal calibration to provide finite‑sample valid inference in a learned representation space. Classical concepts such as stationarity and cyclostationarity naturally emerge as special cases of this framework, and experiments show the method’s sensitivity to distributional changes while maintaining well‑calibrated inference under stable conditions.

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